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Mobile Multimedia Communications. 16th EAI International Conference, MobiMedia 2023, Guilin, China, July 22-24, 2023, Proceedings

Research Article

Improvements Towards the Sonar Image Dataset for Yolov7

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BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-60347-1_6,
        author={Guohao Xie and Jianxun Tang and Zhe Chen and Mingsong Chen},
        title={Improvements Towards the Sonar Image Dataset for Yolov7},
        proceedings={Mobile Multimedia Communications. 16th EAI International Conference, MobiMedia 2023, Guilin, China, July 22-24, 2023, Proceedings},
        proceedings_a={MOBIMEDIA},
        year={2024},
        month={10},
        keywords={SEnet attention FReLU ODConv sonar image target recognition},
        doi={10.1007/978-3-031-60347-1_6}
    }
    
  • Guohao Xie
    Jianxun Tang
    Zhe Chen
    Mingsong Chen
    Year: 2024
    Improvements Towards the Sonar Image Dataset for Yolov7
    MOBIMEDIA
    Springer
    DOI: 10.1007/978-3-031-60347-1_6
Guohao Xie1, Jianxun Tang1, Zhe Chen2,*, Mingsong Chen1
  • 1: School of Ocean Engineering, Guilin University of Electronic Technology
  • 2: School of Information and Communication, Guilin University of Electronic Technology
*Contact email: chenzhe@mail.nwpu.edu.cn

Abstract

Sonar imaging technology has been continuously improving, leading to its widespread use in recognizing underwater targets. However, sonar images often suffer from low contrast, blurred edges, and high noise, which can make it difficult to extract target information during deep learing image feature extraction. This can result in the loss of target features and ultimately affect recognition accuracy. To address the issue at hand, we propose the addition of dynamic ODConv to the original yolov7 model. This will help tackle the problems of error and omission detection wuring complex background extraction and target feature loss in the feature recognition process of sonar images.By incorporating a channel attention mechanism and activation function that are more attuned to spatial features, the feature extraction and target recognition process can avoid the issue of target feature loss, ultimately leading to improved recognition accuracy.

Keywords
SEnet attention FReLU ODConv sonar image target recognition
Published
2024-10-25
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-60347-1_6
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